Assessing the role of polygenic risk scores in cardiovascular risk prediction: a cross-sectional analysis from the Paracelsus 10 000 cohort
Authors' conclusion
Polygenic risk score enhances SCORE2-based prediction of significant CAC.
Study context
- Population
- Participants of the Paracelsus 10,000 cohort with available PGS, SCORE2 and coronary artery calcium (CAC) measurements
- Participants
- 1,420 participants
- Exposure
- SCORE2 + PGS vs SCORE2 alone
- Outcome
- Prediction of significant subclinical coronary atherosclerosis (CAC > 100)
Main finding
As reported in the abstract
Prediction of CAC > 100
NRI
0.649
women
NRI
0.450
men
SCORE2
- AUC, women
- 0.662
- AUC, men
- 0.659
SCORE2 + PGS
- AUC, women
- 0.738
- AUC, men
- 0.714
AUC, women
SCORE20.662
SCORE2 + PGS0.738
AUC, men
SCORE20.659
SCORE2 + PGS0.714
Observational data: associations, not effects.
Further findings
Classification accuracy for CAC > 100 by PGS quintile
- Adding PGS, particularly in the highest quintiles, significantly enhanced classification accuracy
Decision curve analysis (clinical utility)
- PGS as a continuous variable gave the highest net benefit at lower threshold probabilities
Original publication
Assessing the role of polygenic risk scores in cardiovascular risk prediction: a cross-sectional analysis from the Paracelsus 10 000 cohort. Eur J Prev Cardiol. Published 8 April 2025. doi:10.
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